ECE2191 · Probability and AI for engineers

Overview

This unit will introduce fundamental concepts of probability theory applied to engineering problems in a manner that combines intuition and mathematical precision. The treatment of probability includes elementary set operations, sample spaces and probability laws conditional probability, and independence. A discussion of discrete and continuous random variables common distributions, functions, and expectations forms an important part of this unit. You will also learn the law of large numbers and the central limit theorem. In the second half of the unit, the focus shifts to practical machine learning techniques. You will gain hands-on experience in supervised learning methods, ranging from decision trees and random forests to regression analysis. The unit also introduces optimisation theory, crucial for understanding the behaviour of learning algorithms. Furthermore, you will learn about data wrangling and the basics of feedforward neural networks. The unit features application examples from various domains to demonstrate the utility of these mathematical tools in real-world scenarios, including analysing radio telescopy data, images and audio signals.

Areas of study: E3001 Bachelor of Engineering (Honours) - Specialisation: Electrical and computer systems engineering Minor: Artificial intelligence in engineering Minor: Networks for connectivity Minor: Smart manufacturing

Offerings

Assessment

The Handbook lists an examination for this unit.

Continuous assessment: 50% Final assessment: 50% This unit contains hurdle requirements that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Describe concepts and fundamentals of probability theory, such as random variables, probability mass, and density functions.
2. Analyse discrete, continuous and multiple random variables to interpret uncertainty in data.
3. Interpret a comprehensive array of supervised and unsupervised learning techniques, including regression and classification.
4. Apply machine learning algorithms to formulate data-driven decisions for a range of engineering problems.
5. Verify the performance and limitations of various machine learning models in real-world contexts, including regression models and classification techniques.

Workload

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.

Student experience, not official rules. Nothing here changes what the Handbook says.